{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def realignment_data(data,channel_num,factor=2):\n",
    "    \"\"\"\n",
    "    调整输入数据格式\n",
    "    factor: M / D\n",
    "    \"\"\"\n",
    "    D = channel_num // factor\n",
    "    pad = channel_num - D\n",
    "    new_data = np.zeros(pad)\n",
    "    for i in range(0,data.shape[0],D):\n",
    "        new_data = np.append(new_data,data[i:i+D])\n",
    "        new_data = np.append(new_data,data[i+D-pad:i+D])\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = realignment_data(data,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_coeffs0 = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n",
    "Q = win_coeffs0.shape[1]\n",
    "win_coeffs = np.zeros(shape=(4, 2*Q-1))\n",
    "for i in range(4):\n",
    "    for j in range(2*Q-1):\n",
    "        if j % 2 == 0:\n",
    "            win_coeffs[i][j] = win_coeffs0[i][j//2]\n",
    "        else:\n",
    "            win_coeffs[i][j] = 0\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def realignment_data(data,channel_num,factor=2):\n",
    "    \"\"\"\n",
    "    调整输入数据格式\n",
    "    factor: M / D\n",
    "    \"\"\"\n",
    "    D = channel_num // factor\n",
    "    pad = channel_num - D\n",
    "    new_data = np.zeros(pad)\n",
    "    for i in range(0,data.shape[0],D):\n",
    "        new_data = np.append(new_data,data[i:i+D])\n",
    "        # new_data = np.append(new_data,np.flip(data[i+D-pad:i+D]))\n",
    "        new_data = np.append(new_data,data[i+D-pad:i+D])\n",
    "        # print(np.flip(data[i:i+D],axis=0))\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = 8\n",
    "data = np.array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.,20.])\n",
    "data = realignment_data(data,M)\n",
    "disp_len = int(np.ceil(data.size / M))\n",
    "patch_size = int(disp_len * M - data.size)\n",
    "patch_data = np.concatenate((data, np.zeros(patch_size)))\n",
    "\n",
    "# 将数据重新排序为 M行XX列的矩阵   \n",
    "reshape_data = np.reshape(patch_data, (M, -1), order='F')\n",
    "# for i in range(M):\n",
    "#     for j in range(disp_len):\n",
    "#         if j == 0:\n",
    "#             reshape_data[i][j] = data[i]\n",
    "#         else:\n",
    "#             reshape_data[i][j] = data[int(i+j*M/2)]\n",
    "reshape_data_shift = np.zeros(reshape_data.shape)\n",
    "i = j = 0\n",
    "for i in range(reshape_data.shape[1]):\n",
    "    for j in range(M):\n",
    "        index = (M//2)*i+j\n",
    "        shift_index = index % M\n",
    "        reshape_data_shift[shift_index][i] =  reshape_data[j][i]\n",
    "\n",
    "# polyphase_data = np.flipud(reshape_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# polyphase_data_shift\n",
    "reshape_data_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"添加标记信号\"\n",
    "def add_rfi(data,channel_num):\n",
    "    freq = np.fft.fft(data)\n",
    "    freq_width = data.shape[0] // channel_num // 2  #400\n",
    "    step = freq_width // (channel_num + 1) // 2\n",
    "    for i in range(0,channel_num+1):\n",
    "        for j in range(i):\n",
    "            freq[freq_width * (i-1) + step * (j+1)] = 1e4\n",
    "        for j in range(i):\n",
    "            freq[data.shape[0]-(freq_width * (i-1) + step * (j+1))] = 1e4\n",
    "    return np.fft.ifft(freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_num = 16\n",
    "data = np.loadtxt(\"text.txt\")\n",
    "data = add_rfi(data,channel_num)\n",
    "figure = plt.figure(figsize=(20,8))\n",
    "plt.plot(np.abs(np.fft.fft(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''添加标记信号'''\n",
    "def add_flag(data,channel_num):\n",
    "    fs = 640e6\n",
    "    valid_bandwidth = fs/channel_num/2;\n",
    "    Amp = 10\n",
    "    t = np.arange(0, (1 / fs * 12800), 1 / fs)\n",
    "    for index in range():\n",
    "            data[index] += Amp * math.sin( math.pi * 40.3 * t[index])\n",
    "            data[index] += Amp * math.sin( math.pi * 46e6 * t[index])\n",
    "\n",
    "            data[index] += Amp * math.sin(math.pi * 51e6 * t[index])\n",
    "            data[index] += Amp * math.sin(math.pi * 98e6 * t[index])\n",
    "#     np.savetxt(\"data_rfi2.txt\", data)\n",
    "    xn = np.fft.fft(data)\n",
    "    t = np.arange(0, (fs + fs/12799), (fs/12799)) + 1182e6\n",
    "    plt.plot(t, abs(xn/12800))\n",
    "    plt.show() \n",
    "   \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_flag(data,channel_num = 16, fs = 800e6):\n",
    "    delta_freq = 2e6\n",
    "    Amp = 10\n",
    "    freq_width = fs//channel_num\n",
    "    t = np.arange(0, (1 / fs * 12800), 1 / fs)\n",
    "    for i in range(channel_num):\n",
    "        for j in range(1,i+1):\n",
    "            for index,value in enumerate(data):\n",
    "                data[index] +=  Amp * math.sin(2 * math.pi * t[index] * (freq_width*(i-1) + delta_freq * (j+1)))\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.loadtxt(\"text.txt\")\n",
    "data = add_flag(data)\n",
    "figure = plt.figure(figsize=(20,8))\n",
    "plt.plot(np.abs(np.fft.fft(data))[:6400])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.fftpack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = numpy.loadtxt(\"text.txt\")  # 1182MHz-1582MHz\n",
    "fs = 6400                         # ADC Sample Rate\n",
    "t = numpy.arange(0, (1 / fs * 12800), 1 / fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_num = 8\n",
    "freq_num = data.shape[0] // channel_num // 2; # 每个子带有多少点数\n",
    "for index, value in enumerate(data):\n",
    "        for i in range(1,channel_num + 1):\n",
    "                step = freq_num // (i + 1)\n",
    "                for j in range(i):\n",
    "                        freq = freq_num * (i-1) + step * (j+1)\n",
    "                        data[index] += 2* math.sin( math.pi * freq * t[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xn = scipy.fftpack.fft(data)\n",
    "plt.figure(figsize=(30,8))\n",
    "plt.plot(abs(xn/12800))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
  },
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
